Multi-Objective DNN-Based Precoder for MIMO Communications
This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. Firs...
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| Vydáno v: | IEEE transactions on communications Ročník 69; číslo 7; s. 4476 - 4488 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0090-6778, 1558-0857 |
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| Abstract | This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. First, a rotation-based precoder is developed to solve the above problems independently. Rotation-based precoding is a new precoding and power allocation scheme that beats existing solutions for PHY security and multicasting and is reliable in different antenna settings. Next, a DNN-based precoder is designed to unify the solution for all objectives. The proposed DNN concurrently learns the solutions given by conventional methods, i.e., analytical or rotation-based solutions. A binary vector is designed as an input feature to distinguish the objectives. Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99.45% of the averaged optimal solutions). The new precoder is also more robust to the variations of the numbers of antennas at the receivers. |
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| AbstractList | This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. First, a rotation-based precoder is developed to solve the above problems independently. Rotation-based precoding is a new precoding and power allocation scheme that beats existing solutions for PHY security and multicasting and is reliable in different antenna settings. Next, a DNN-based precoder is designed to unify the solution for all objectives. The proposed DNN concurrently learns the solutions given by conventional methods, i.e., analytical or rotation-based solutions. A binary vector is designed as an input feature to distinguish the objectives. Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99.45% of the averaged optimal solutions). The new precoder is also more robust to the variations of the numbers of antennas at the receivers. |
| Author | Zhang, Xinliang Vaezi, Mojtaba |
| Author_xml | – sequence: 1 givenname: Xinliang orcidid: 0000-0002-0672-2907 surname: Zhang fullname: Zhang, Xinliang email: xzhang4@villanova.edu organization: Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, USA – sequence: 2 givenname: Mojtaba orcidid: 0000-0003-3357-4660 surname: Vaezi fullname: Vaezi, Mojtaba email: mvaezi@villanova.edu organization: Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, USA |
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| SubjectTerms | Antennas Artificial neural networks beamforming Data transmission Deep learning Energy harvesting MIMO MIMO communication Multicast communication Multicasting Multiple objective analysis Optimization physical layer Power transfer Precoding Robustness (mathematics) Rotation Security SWIPT Wireless communication wiretap channel |
| Title | Multi-Objective DNN-Based Precoder for MIMO Communications |
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